Why Normalization Is the Hardest Part of Data Extraction
Data extraction is often described as a technical process: selecting fields, validating formats, and producing structured outputs. In practice, the most difficult part of extraction is not accessing data or defining schemas, but normalizing inconsistent records into a coherent dataset. Normalization
Data Extraction vs Data Transformation: Where the Boundary Actually Is
Data extraction and data transformation are often discussed together, and in many systems they are implemented close to one another. This proximity makes the boundary between them easy to blur, especially in growing data pipelines. However, extraction and transformation solve different
From Scraping to Usable Datasets: What Actually Happens in Between
Web scraping is often discussed as the act of collecting data from websites. In practice, collecting data is only the beginning. The more difficult work begins after pages have been accessed and raw records have been retrieved. The gap between scraped
Why Schema Drift Breaks Datasets Over Time
Schema drift is one of the most common reasons data systems degrade quietly over time. It rarely causes immediate failures, but it steadily erodes data quality, consistency, and trust—often without being noticed until downstream processes begin to break. Understanding schema drift